Course detail

Probability,statistics,operations research

FEKT-LPSOAcad. year: 2012/2013

Basic statistical tests - t-test, F-test. Confidence intervals. Linear regression. Post-hoc tests. Goodnessw of fit test. Nonparametric tests. Mathematical methods in economics - linear programming, the transport problem. Dynamic programming, recursive algorithm, inventory models.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. RNDr. Jaromír Baštinec, CSc.

Department

Department of Mathematics (UMAT)

Learning outcomes of the course unit

The graduate of this course should be able to solve the optimising and statistical problems of technical and economical practice.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The objecive of the course is to enlarge the knowledge in the area of statistical tests and confidence intervals, to show some spheres of mathematical thinking in economics and to introduce the concepts of recursive algorithms.

Specification of controlled education, way of implementation and compensation for absences

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-ML Master's

    branch ML-EVM , 1 year of study, winter semester, theoretical subject

  • Programme EEKR-ML Master's

    branch ML-EVM , 1 year of study, winter semester, theoretical subject

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, theoretical subject

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. RNDr. Jaromír Baštinec, CSc.

Syllabus

1. Parameter estimation, t-test, confidence intervals.
2. Analysis of variance (ANOVA).
3. Correlation, regression.
4. After or instead of ANOVA.
5. Chi square distribution.
6. Nonparametric tests.
7. Linear programming.
8. Duality in linear programming.
9. Transport problem.
10.Dynamic programming.
11.Inventory models.
12.Probabilistic dynamic programming.

Exercise in computer lab

18 hod., compulsory

Teacher / Lecturer

doc. RNDr. Jaromír Baštinec, CSc.

Syllabus

In accordance with the lecture.